{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data preparation for estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Dropbox\\WB Project\\Data\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "pd.options.display.float_format = '{:,.1f}'.format\n",
    "%cd \"C:\\Users\\User\\Dropbox\\WB Project\\Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fuel Type\n",
       "Diesel            121.1\n",
       "Electric w. REX    13.0\n",
       "Electric w/oREX     1.2\n",
       "HEV/Dsl.PlugIn     43.0\n",
       "HEV/Petr.          59.5\n",
       "HEV/Petr.PlugIn    39.7\n",
       "HEV/Petr.Unsp.    115.6\n",
       "Hybrid/Unspec.    144.0\n",
       "LPG (Petr.gas)    144.0\n",
       "MEV/Petrol        152.4\n",
       "Natural Gas       126.1\n",
       "Petrol            153.0\n",
       "Name: Emissions, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data=pd.read_excel(\"Col_data_F.xlsx\")\n",
    "col_data=col_data.drop(col_data[col_data['Global Sales Sub-Segment'] =='Van' ].index)\n",
    "col_data=col_data.drop(col_data[col_data['Global Sales Sub-Segment'] =='PUP' ].index)\n",
    "col_data=col_data.drop(col_data[col_data['Year'] ==2021 ].index)\n",
    "col_data=col_data.drop(col_data[col_data['MSRPDA'] >150000 ].index)\n",
    "col_data.keys()\n",
    "col_data.groupby('Fuel Type')['Emissions'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create some functions to construct instruments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cont_var_inst(df,var,opr,gr,sf):\n",
    "    \"\"\"\" This function creates BLP instrumets for\n",
    "    continous variables \"\"\"\n",
    "    for vr in var:\n",
    "        for op in opr:\n",
    "            nam=vr +'_'+ op +'_comp_'+sf\n",
    "            df[nam]=df.groupby(gr)[vr].transform(op)-df[vr]\n",
    "    return(df)\n",
    "\n",
    "def sim_var_inst(df,var,st,gr):\n",
    "    \"\"\"\" This function creates BLP instrumets for\n",
    "    counting simmilar products \"\"\"\n",
    "    if st==1:\n",
    "        for vr in var:\n",
    "            ls=gr.copy()\n",
    "            ls.append(vr)\n",
    "            nam=vr +'_idn'+'_comp'\n",
    "            df[nam]=df.groupby(ls)['Version'].transform('count')-1\n",
    "    if st==2:\n",
    "        ls2=list(zip(var[::2], var[1::2]))\n",
    "        for vr1, vr2 in ls2:\n",
    "            ls=gr+[vr1,vr2]\n",
    "            nam=vr1 +'_'+vr2+'_idn'+'_comp'\n",
    "            df[nam]=df.groupby(ls)['Version'].transform('count')-1\n",
    "    if st==3:\n",
    "        ls2=list(zip(var[::2], var[1::2],var[2::3]))\n",
    "        for vr1, vr2, vr3 in ls2:\n",
    "            ls=gr+[vr1,vr2,vr3]\n",
    "            nam=vr1 +'_'+vr2+'_'+vr3+'_idn'+'_comp'\n",
    "            df[nam]=df.groupby(ls)['Version'].transform('count')-1\n",
    "    if st>3:\n",
    "        print('max characteristics combination iqual to 3')\n",
    "        \n",
    "    return(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regression to estimate emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_X_col=col_data[['VerYearD','Emissions','Engine (ccm)','Engine (HP)', 'Transmission','Turbo','Axle Configuration','Make','Standard Segmentation','Fuel Type',\n",
    "              'Length class Segmentation','Body Type','Driven Wheels','Year']]\n",
    "df_X_col=pd.get_dummies(df_X_col,columns=['Make','Standard Segmentation','Fuel Type','Length class Segmentation','Body Type',\n",
    "                                  'Driven Wheels','Year'], prefix=\"\", prefix_sep=\"\",drop_first=True)\n",
    "df_X_col_Tr=df_X_col.dropna()\n",
    "df_Y_col=df_X_col_Tr.Emissions\n",
    "df_X_col_T=df_X_col_Tr.drop(['Emissions','VerYearD'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7908709510820185"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_col = LinearRegression(fit_intercept=False).fit(df_X_col_T, df_Y_col)\n",
    "coef = pd.DataFrame(reg_col.coef_, df_X_col_T.columns, columns=['Coefficients'])\n",
    "reg_col.score(df_X_col_T, df_Y_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill emissions nans usign predictions from linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_est=df_X_col.drop(['Emissions'],axis=1)\n",
    "df_est=df_est.dropna()\n",
    "aa = reg_col.predict(df_est.drop(['VerYearD'],axis=1))\n",
    "reg_nan=pd.DataFrame(data=aa, index=df_est.index, columns=['Emi_reg'])\n",
    "col_data['Emi_reg']=reg_nan['Emi_reg']\n",
    "col_data['Emissions'].fillna(col_data['Emi_reg'], inplace=True)\n",
    "col_data=col_data.dropna(subset=['Emissions'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create instruments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "var=['No. of Gears' ,'Engine (ccm)','Engine (HP)','Gross Vehicle Weight','Emissions']\n",
    "var2=['Turbo','Transmission','Driven Wheels','Axle Configuration']\n",
    "opr=['sum','mean']\n",
    "gr=['Year','Month','Standard Segmentation']\n",
    "gr2=['Year','Standard Segmentation','Make']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data=cont_var_inst(col_data,var,opr,gr,'AL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data=cont_var_inst(col_data,var,opr,gr2,'FR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data=sim_var_inst(col_data,var2,1,gr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['Num_frm_sta_seg']=col_data.groupby(['Year','Month','Make','Standard Segmentation'])['Version'].transform('count')-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['Num_mod']=col_data.groupby(['Year','Month','Make','Model'])['Version'].transform('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['Num_mod_frm']=col_data.groupby(['Year','Month','Make'])['Version'].transform('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['Num_mod_fc']=col_data.groupby(['Year','Month','Make','Fuel Cat'])['Version'].transform('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_col=pd.get_dummies(col_data,columns=['Make','Standard Segmentation','Fuel Type','Length class Segmentation','Body Type',\n",
    "                                  'Driven Wheels','Year'], prefix=\"d\", prefix_sep=\"_\",drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_col=col_data[['Make','Standard Segmentation','Fuel Type','Length class Segmentation','Body Type','Driven Wheels','Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data=pd.concat([d_col,var_col], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import data on potential market, gas price, number of charging stations, us exchange rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['Year-Month']= [i +'-'+ j for i, j in zip(col_data['Year'].apply(str), col_data['Month'].apply(str))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['EXC']=np.nan # Exchange rate\n",
    "col_data['CE']=np.nan # Charging estations\n",
    "col_data['GAS']=np.nan # Gas price per galon\n",
    "col_data['TM']=np.nan # Total Market\n",
    "col_data['ELEC']=np.nan # Price of electricity KW/H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "ER=pd.read_csv('ER.csv')\n",
    "ER=ER.set_index('Year-Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['EXC']=col_data['EXC'].fillna(col_data['Year-Month']).map(ER['ER'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "CE=pd.read_csv('CE.csv')\n",
    "CE=CE.set_index('Year-Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['CE']=col_data['CE'].fillna(col_data['Year-Month']).map(CE['CE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "GAS=pd.read_csv('GAS.csv')\n",
    "GAS=GAS.set_index('Year-Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['GAS']=col_data['GAS'].fillna(col_data['Year-Month']).map(GAS['GAS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TMO</th>\n",
       "      <th>TM</th>\n",
       "      <th>TM2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year-Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-1</th>\n",
       "      <td>210,527.8</td>\n",
       "      <td>21053</td>\n",
       "      <td>26316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-2</th>\n",
       "      <td>210,527.8</td>\n",
       "      <td>21053</td>\n",
       "      <td>26316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-3</th>\n",
       "      <td>210,527.8</td>\n",
       "      <td>21053</td>\n",
       "      <td>26316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-4</th>\n",
       "      <td>210,527.8</td>\n",
       "      <td>21053</td>\n",
       "      <td>26316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-5</th>\n",
       "      <td>210,527.8</td>\n",
       "      <td>21053</td>\n",
       "      <td>26316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TMO     TM    TM2\n",
       "Year-Month                        \n",
       "2015-1     210,527.8  21053  26316\n",
       "2015-2     210,527.8  21053  26316\n",
       "2015-3     210,527.8  21053  26316\n",
       "2015-4     210,527.8  21053  26316\n",
       "2015-5     210,527.8  21053  26316"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TM=pd.read_csv('TM.csv')\n",
    "TM=TM.set_index('Year-Month')\n",
    "TM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    21053\n",
       "2    21053\n",
       "3    22303\n",
       "4    21053\n",
       "5    21053\n",
       "Name: TM, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data['TM']=col_data['TM'].fillna(col_data['Year-Month']).map(TM['TM'])\n",
    "col_data['TM'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ELEC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year-Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-1</th>\n",
       "      <td>257.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-2</th>\n",
       "      <td>253.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-3</th>\n",
       "      <td>254.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-4</th>\n",
       "      <td>268.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-5</th>\n",
       "      <td>260.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ELEC\n",
       "Year-Month      \n",
       "2015-1     257.3\n",
       "2015-2     253.8\n",
       "2015-3     254.5\n",
       "2015-4     268.2\n",
       "2015-5     260.1"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ELEC=pd.read_csv('ELEC.csv')\n",
    "ELEC=ELEC.set_index('Year-Month')\n",
    "ELEC.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   273.2\n",
       "2   254.5\n",
       "3   339.4\n",
       "4   299.0\n",
       "5   266.4\n",
       "Name: ELEC, dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data['ELEC']=col_data['ELEC'].fillna(col_data['Year-Month']).map(ELEC['ELEC'])\n",
    "col_data['ELEC'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute outside option and market shares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['Total Sales']=col_data.groupby(['Year','Month'])['Q'].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['s0']=1-col_data['Total Sales']/col_data['TM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['sj']=col_data['Q']/col_data['TM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16a453d4e48>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(col_data[\"MSRPDA\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16a431dcc18>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(col_data[col_data['Fuel Cat']=='Electric'][\"MSRPDA\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16a4319ecc0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(col_data[col_data['Fuel Cat']=='Hybrid'][\"MSRPDA\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16a43147cc0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(col_data[col_data['Fuel Cat']=='NR'][\"MSRPDA\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   2.6\n",
       "2   3.0\n",
       "3   2.9\n",
       "4   2.4\n",
       "5   2.6\n",
       "Name: GAS_D, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data['GAS_D']=col_data['GAS']/col_data['EXC'] # Gas price in USD\n",
    "col_data['TM'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   0.1\n",
       "2   0.1\n",
       "3   0.1\n",
       "4   0.1\n",
       "5   0.1\n",
       "Name: ELEC_D, dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data['ELEC_D']=col_data['ELEC']/col_data['EXC'] # Electricity price in USD\n",
    "col_data['ELEC_D'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['Fuel_Ef']=1000/(col_data['Emissions']*1/8.5) # MPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   41.0\n",
       "2   47.8\n",
       "3   49.0\n",
       "4   63.4\n",
       "5   63.4\n",
       "Name: Fuel_Ef, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data['Fuel_Ef'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fuel Cat\n",
       "Electric     inf\n",
       "GAS         61.6\n",
       "Hybrid     169.8\n",
       "NR          57.1\n",
       "Name: Fuel_Ef, dtype: float64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data.groupby('Fuel Cat')['Fuel_Ef'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fuel Cat\n",
       "Electric   3.2\n",
       "GAS        4.7\n",
       "Hybrid     2.4\n",
       "NR         5.2\n",
       "Name: Consumption, dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data['Consumption']=col_data['GAS_D']*100/col_data['Fuel_Ef']+(col_data['ELEC_D']*col_data['Kwh/100miles'])\n",
    "col_data.groupby('Fuel Cat')['Consumption'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['Power']=np.log(col_data['Engine (HP)']/col_data['Gross Vehicle Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   -2.8\n",
       "2   -2.0\n",
       "3   -2.0\n",
       "4   -2.4\n",
       "5   -2.4\n",
       "Name: Power, dtype: float64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data['Power'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['High_end']=(col_data.groupby(['Year','Make','Model'])['Engine (HP)'].transform('max')==col_data['Engine (HP)']).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data['Num_mod']=col_data.groupby(['Year','Month','Make','Model'])['Version'].transform('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data.to_excel(\"Colombia_final.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5232925515085429"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data['s0'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['5 Upper Medium', '4 Medium', '3 Lower Medium', '2 Small',\n",
       "       '7 Sport', '6 Luxury', 'F Off-Road', '1 Mini', 'M MPV'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_data['Standard Segmentation'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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